






"""simple docstring"""


import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	List[Any]=0.0_1     , UpperCAmelCase							:	List[Any]=1000   )	->       Tuple:
											lowerCAmelCase   :Union[str, Any]						=			p_stop
											lowerCAmelCase   :Union[str, Any]						=			max_length
					def __iter__(							self							:	Optional[Any]   )	->       Any:
											lowerCAmelCase   :Dict						=			0
											lowerCAmelCase   :List[str]						=			False
											while not stop and count < self.max_length:
																	yield count
																	count += 1
																	lowerCAmelCase   :Dict						=			random.random() < self.p_stop




class 						__UpperCamelCase     (     unittest.TestCase						):
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	str=False     , UpperCAmelCase							:	str=True   )	->       str:
											lowerCAmelCase   :int						=			[
											    BatchSamplerShard(UpperCAmelCase     , 2     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )
											    for i in range(2   )
											]
											lowerCAmelCase   :List[str]						=			[list(UpperCAmelCase   ) for batch_sampler_shard in batch_sampler_shards]
											if not split_batches:
																	self.assertListEqual([len(UpperCAmelCase   ) for shard in batch_sampler_shards]     , [len(UpperCAmelCase   ) for e in expected]   )
											self.assertListEqual(UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Any:
											# Check the shards when the dataset is a round multiple of total batch size.
											lowerCAmelCase   :int						=			BatchSampler(range(24   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											lowerCAmelCase   :Any						=			BatchSampler(range(24   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											# Check the shards when the dataset is a round multiple of batch size but not total batch size.
											lowerCAmelCase   :Dict						=			BatchSampler(range(21   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											lowerCAmelCase   :Union[str, Any]						=			BatchSampler(range(21   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
											# num_processes batch.
											lowerCAmelCase   :int						=			BatchSampler(range(22   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :int						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			BatchSampler(range(22   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
											# num_processes batch.
											lowerCAmelCase   :Tuple						=			BatchSampler(range(20   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											lowerCAmelCase   :str						=			BatchSampler(range(20   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											# Check the shards when the dataset is very small.
											lowerCAmelCase   :Tuple						=			BatchSampler(range(2   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[[[0, 1, 0]], [[1, 0, 1]]]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			BatchSampler(range(2   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			[[], []]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Any:
											# Check the shards when the dataset is a round multiple of batch size.
											lowerCAmelCase   :Any						=			BatchSampler(range(24   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											lowerCAmelCase   :List[str]						=			BatchSampler(range(24   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size.
											lowerCAmelCase   :Dict						=			BatchSampler(range(22   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :str						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											lowerCAmelCase   :List[str]						=			BatchSampler(range(22   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :int						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size or num_processes.
											lowerCAmelCase   :Dict						=			BatchSampler(range(21   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			BatchSampler(range(21   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											# Check the shards when the dataset is very small.
											lowerCAmelCase   :int						=			BatchSampler(range(2   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[[[0, 1]], [[0, 1]]]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			BatchSampler(range(2   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[[], []]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[Any]:
											# Check the shards when the dataset is a round multiple of total batch size.
											lowerCAmelCase   :Tuple						=			BatchSampler(range(24   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Union[str, Any]						=			BatchSampler(range(24   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is a round multiple of batch size but not total batch size.
											lowerCAmelCase   :Any						=			BatchSampler(range(21   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			BatchSampler(range(21   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
											# num_processes batch.
											lowerCAmelCase   :Optional[Any]						=			BatchSampler(range(22   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :str						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			BatchSampler(range(22   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
											# num_processes batch.
											lowerCAmelCase   :Dict						=			BatchSampler(range(20   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :str						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :int						=			BatchSampler(range(20   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :int						=			[
											    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
											    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is very small.
											lowerCAmelCase   :str						=			BatchSampler(range(2   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Any						=			[[[0, 1]], []]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			BatchSampler(range(2   )     , batch_size=3     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :str						=			[[], []]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , even_batches=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[str]:
											# Check the shards when the dataset is a round multiple of batch size.
											lowerCAmelCase   :List[str]						=			BatchSampler(range(24   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			BatchSampler(range(24   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											# Expected shouldn't change
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size.
											lowerCAmelCase   :List[str]						=			BatchSampler(range(22   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			BatchSampler(range(22   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is not a round multiple of batch size or num_processes.
											lowerCAmelCase   :str						=			BatchSampler(range(21   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			BatchSampler(range(21   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			[
											    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
											    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
											]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											# Check the shards when the dataset is very small.
											lowerCAmelCase   :str						=			BatchSampler(range(2   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :List[Any]						=			[[[0, 1]], []]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )

											lowerCAmelCase   :Union[str, Any]						=			BatchSampler(range(2   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			[[], []]
											self.check_batch_sampler_shards(UpperCAmelCase     , UpperCAmelCase     , split_batches=UpperCAmelCase     , even_batches=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Tuple:
											lowerCAmelCase   :Dict						=			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
											lowerCAmelCase   :str						=			[BatchSamplerShard(UpperCAmelCase     , 2     , UpperCAmelCase     , even_batches=UpperCAmelCase   ) for i in range(2   )]

											self.assertEqual(len(batch_sampler_shards[0]   )     , 3   )
											self.assertEqual(len(batch_sampler_shards[1]   )     , 2   )

											self.assertListEqual(list(batch_sampler_shards[0]   )     , [[0, 1, 2], [5, 6, 7, 8], [12, 13]]   )
											self.assertListEqual(list(batch_sampler_shards[1]   )     , [[3, 4], [9, 10, 11]]   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	str     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	Tuple=False     , UpperCAmelCase							:	Optional[Any]=2     , UpperCAmelCase							:	str=False   )	->       Union[str, Any]:
											random.seed(UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			list(UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			[
											    IterableDatasetShard(
											        UpperCAmelCase     , batch_size=UpperCAmelCase     , drop_last=UpperCAmelCase     , num_processes=UpperCAmelCase     , process_index=UpperCAmelCase     , split_batches=UpperCAmelCase     , )
											    for i in range(UpperCAmelCase   )
											]
											lowerCAmelCase   :List[str]						=			[]
											for iterable_dataset_shard in iterable_dataset_shards:
																	# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
																	random.seed(UpperCAmelCase   )
																	iterable_dataset_lists.append(list(UpperCAmelCase   )   )

											lowerCAmelCase   :Any						=			batch_size // num_processes if split_batches else batch_size
											# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
											lowerCAmelCase   :List[Any]						=			iterable_dataset_lists[0]
											for l in iterable_dataset_lists[1:]:
																	self.assertEqual(len(UpperCAmelCase   )     , len(UpperCAmelCase   )   )
																	self.assertTrue(len(UpperCAmelCase   ) % shard_batch_size == 0   )

											lowerCAmelCase   :int						=			[]
											for idx in range(0     , len(UpperCAmelCase   )     , UpperCAmelCase   ):
																	for l in iterable_dataset_lists:
																							observed += l[idx : idx + shard_batch_size]

											if not drop_last:
																	while len(UpperCAmelCase   ) < len(UpperCAmelCase   ):
																							reference += reference
											self.assertListEqual(UpperCAmelCase     , reference[: len(UpperCAmelCase   )]   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       int:
											lowerCAmelCase   :List[Any]						=			42
											lowerCAmelCase   :Optional[Any]						=			RandomIterableDataset()

											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )

											# Edge case with a very small dataset
											lowerCAmelCase   :List[Any]						=			RandomIterableDataset(max_length=2   )

											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
											self.check_iterable_dataset_shards(UpperCAmelCase     , UpperCAmelCase     , batch_size=4     , drop_last=UpperCAmelCase     , split_batches=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Any:
											lowerCAmelCase   :Any						=			BatchSampler(range(16   )     , batch_size=4     , drop_last=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			SkipBatchSampler(UpperCAmelCase     , 2   )
											self.assertListEqual(list(UpperCAmelCase   )     , [[8, 9, 10, 11], [12, 13, 14, 15]]   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[Any]:
											lowerCAmelCase   :int						=			SkipDataLoader(list(range(16   )   )     , batch_size=4     , skip_batches=2   )
											self.assertListEqual([t.tolist() for t in dataloader]     , [[8, 9, 10, 11], [12, 13, 14, 15]]   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Any:
											lowerCAmelCase   :Tuple						=			DataLoader(list(range(16   )   )     , batch_size=4   )
											lowerCAmelCase   :Optional[int]						=			skip_first_batches(UpperCAmelCase     , num_batches=2   )
											self.assertListEqual([t.tolist() for t in new_dataloader]     , [[8, 9, 10, 11], [12, 13, 14, 15]]   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       int:
											lowerCAmelCase   :Optional[Any]						=			DataLoaderShard(list(range(16   )   )     , batch_size=4   )
											for idx, _ in enumerate(UpperCAmelCase   ):
																	self.assertEqual(dataloader.end_of_dataloader     , idx == 3   )

											# Test it also works on the second iteration
											for idx, _ in enumerate(UpperCAmelCase   ):
																	self.assertEqual(dataloader.end_of_dataloader     , idx == 3   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Any:
											Accelerator()
											lowerCAmelCase   :Optional[int]						=			DataLoaderDispatcher(range(16   )     , batch_size=4   )
											for idx, _ in enumerate(UpperCAmelCase   ):
																	self.assertEqual(dataloader.end_of_dataloader     , idx == 3   )

											# Test it also works on the second iteration
											for idx, _ in enumerate(UpperCAmelCase   ):
																	self.assertEqual(dataloader.end_of_dataloader     , idx == 3   )







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")